BERT based Adverse Drug Effect Tweet Classification

Tanay Kayastha; Pranjal Gupta; Pushpak Bhattacharyya

doi:10.18653/v1/2021.smm4h-1.15

BERT based Adverse Drug Effect Tweet Classification

Tanay Kayastha, Pranjal Gupta, Pushpak Bhattacharyya

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Abstract

This paper describes models developed for the Social Media Mining for Health (SMM4H) 2021 shared tasks. Our team participated in the first subtask that classifies tweets with Adverse Drug Effect (ADE) mentions. Our best performing model utilizes BERTweet followed by a single layer of BiLSTM. The system achieves an F-score of 0.45 on the test set without the use of any auxiliary resources such as Part-of-Speech tags, dependency tags, or knowledge from medical dictionaries.

Anthology ID:: 2021.smm4h-1.15
Volume:: Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
Month:: June
Year:: 2021
Address:: Mexico City, Mexico
Editors:: Arjun Magge, Ari Klein, Antonio Miranda-Escalada, Mohammed Ali Al-garadi, Ilseyar Alimova, Zulfat Miftahutdinov, Eulalia Farre-Maduell, Salvador Lima Lopez, Ivan Flores, Karen O'Connor, Davy Weissenbacher, Elena Tutubalina, Abeed Sarker, Juan M Banda, Martin Krallinger, Graciela Gonzalez-Hernandez
Venue:: SMM4H
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 88–90
Language:
URL:: https://aclanthology.org/2021.smm4h-1.15/
DOI:: 10.18653/v1/2021.smm4h-1.15
Bibkey:
Cite (ACL):: Tanay Kayastha, Pranjal Gupta, and Pushpak Bhattacharyya. 2021. BERT based Adverse Drug Effect Tweet Classification. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 88–90, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):: BERT based Adverse Drug Effect Tweet Classification (Kayastha et al., SMM4H 2021)
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PDF:: https://aclanthology.org/2021.smm4h-1.15.pdf

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@inproceedings{kayastha-etal-2021-bert,
    title = "{BERT} based Adverse Drug Effect Tweet Classification",
    author = "Kayastha, Tanay  and
      Gupta, Pranjal  and
      Bhattacharyya, Pushpak",
    editor = "Magge, Arjun  and
      Klein, Ari  and
      Miranda-Escalada, Antonio  and
      Al-garadi, Mohammed Ali  and
      Alimova, Ilseyar  and
      Miftahutdinov, Zulfat  and
      Farre-Maduell, Eulalia  and
      Lopez, Salvador Lima  and
      Flores, Ivan  and
      O'Connor, Karen  and
      Weissenbacher, Davy  and
      Tutubalina, Elena  and
      Sarker, Abeed  and
      Banda, Juan M  and
      Krallinger, Martin  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the Sixth Social Media Mining for Health ({\#}SMM4H) Workshop and Shared Task",
    month = jun,
    year = "2021",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.smm4h-1.15/",
    doi = "10.18653/v1/2021.smm4h-1.15",
    pages = "88--90",
    abstract = "This paper describes models developed for the Social Media Mining for Health (SMM4H) 2021 shared tasks. Our team participated in the first subtask that classifies tweets with Adverse Drug Effect (ADE) mentions. Our best performing model utilizes BERTweet followed by a single layer of BiLSTM. The system achieves an F-score of 0.45 on the test set without the use of any auxiliary resources such as Part-of-Speech tags, dependency tags, or knowledge from medical dictionaries."
}

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%0 Conference Proceedings
%T BERT based Adverse Drug Effect Tweet Classification
%A Kayastha, Tanay
%A Gupta, Pranjal
%A Bhattacharyya, Pushpak
%Y Magge, Arjun
%Y Klein, Ari
%Y Miranda-Escalada, Antonio
%Y Al-garadi, Mohammed Ali
%Y Alimova, Ilseyar
%Y Miftahutdinov, Zulfat
%Y Farre-Maduell, Eulalia
%Y Lopez, Salvador Lima
%Y Flores, Ivan
%Y O’Connor, Karen
%Y Weissenbacher, Davy
%Y Tutubalina, Elena
%Y Sarker, Abeed
%Y Banda, Juan M.
%Y Krallinger, Martin
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
%D 2021
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F kayastha-etal-2021-bert
%X This paper describes models developed for the Social Media Mining for Health (SMM4H) 2021 shared tasks. Our team participated in the first subtask that classifies tweets with Adverse Drug Effect (ADE) mentions. Our best performing model utilizes BERTweet followed by a single layer of BiLSTM. The system achieves an F-score of 0.45 on the test set without the use of any auxiliary resources such as Part-of-Speech tags, dependency tags, or knowledge from medical dictionaries.
%R 10.18653/v1/2021.smm4h-1.15
%U https://aclanthology.org/2021.smm4h-1.15/
%U https://doi.org/10.18653/v1/2021.smm4h-1.15
%P 88-90

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Markdown (Informal)

[BERT based Adverse Drug Effect Tweet Classification](https://aclanthology.org/2021.smm4h-1.15/) (Kayastha et al., SMM4H 2021)

BERT based Adverse Drug Effect Tweet Classification (Kayastha et al., SMM4H 2021)

ACL

Tanay Kayastha, Pranjal Gupta, and Pushpak Bhattacharyya. 2021. BERT based Adverse Drug Effect Tweet Classification. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 88–90, Mexico City, Mexico. Association for Computational Linguistics.